Tele‑Physiotherapy Platforms: A Review-Scilight

Digital Technologies Research and Applications

Review

Tele‑Physiotherapy Platforms: A Review

Downloads

https://doi.org/10.54963/dtra.v4i2.1305
Kavallieratos, G., & Kavallieratou, E. (2025). Tele‑Physiotherapy Platforms: A Review. Digital Technologies Research and Applications, 4(2), 168–181. https://doi.org/10.54963/dtra.v4i2.1305
Volume 4 Issue 2: September (In Progress)
Copyright (c) 2025 Georgios Kavallieratos, Ergina Kavallieratou
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Digital Technologies Research and Applications (DTRA)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by DTRA agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source are properly cited, anyone may copy, redistribute, reuse, and transform the content.

Authors

  • Georgios Kavallieratos

    Artificial Intelligence Laboratory, Department of Information and Communication Systems Engineering, University of the Aegean, Karlovasi, Samos 83200, Greece
  • Ergina Kavallieratou

    Artificial Intelligence Laboratory, Department of Information and Communication Systems Engineering, University of the Aegean, Karlovasi, Samos 83200, Greece

Received: 11 June 2025; Revised: 20 July 2025; Accepted: 23 July 2025; Published: 8 August 2025

Physiotherapy has been a subject of research, targeting the facilitation of monitoring and remote assistance provided by the physiotherapist, whenever collocation of doctor and patient is not possible. This is known as tele‑physiotherapy, or tele‑rehabilitation. It has evolved significantly over the past few decades due to advancements in technology, but has also become a necessity during particular periods, for example, during the COVID‑19 pandemic. Initially, tele‑physiotherapy was mostly performed using video conferencing and mainstream telecommunication services; however, recent developments have seen the emergence of sophisticated tele‑physiotherapy platforms that integrate various technologies such as sensors, wearable devices, and digital health platforms. Moreover, the incorporation of Machine Learning (ML) and Artificial Intelligence (AI) enables real‑time analysis, treatment personalization, and the introduction of feedback mechanisms, thus improving the usability and efficiency of rehabilitation sessions. Tele‑physiotherapy solutions address the scope from a wide variety of aspects and demonstrate variable effectiveness. This review examines 19 studies and solutions and compares 14 of those proposals that offer a tele‑physiotherapy solution for patients. Although their services, complexity and functionality vary, the basic criterion for inclusion in the evaluation was that they offer a tele‑physiotherapy service as a part of their main scope. Some of those solutions are commercially available, some are at a research level, but all of them were primarily addressed from the aspect of usability.

Keywords:

Tele‑Physiotherapy Remote Rehabilitation Wearable Sensors Machine Learning

References

  1. Cottrell, M.A.; Galea, O.A.; O’Leary, S.P.; et al. Real-time telerehabilitation for the treatment of musculoskeletal conditions is effective and comparable to standard practice: a systematic review and meta-analysis. Clin. Rehabil. 2016, 31, 625–638. DOI: https://doi.org/10.1177/0269215516645148
  2. Moffet, H.; Tousignant, M.; Nadeau, S.; et al. Patient satisfaction with in-home telerehabilitation after total knee arthroplasty: results from a randomized controlled trial. Telemed. e-Health 2017, 23, 80–87.
  3. Sarfo, F.S.; Ulasavets, U.; Opare-Sem, O.K.; et al. Tele-rehabilitation after stroke: an updated systematic review of the literature. J. Stroke Cerebrovasc. Dis. 2018, 27, 2306–2318.
  4. Kakegawa, K.; Matsuda, T. Challenges and Prospects of Sensing Technology for the Promotion of Tele-Physiotherapy: A Narrative Review. Sensors 2024, 25, 16.
  5. Rashidieranjbar, F.; Farhadi, A.; Zamanifar, A. Revolutionizing Healthcare with Generative Artificial Intelligence Technologies. In Generative Artificial Intelligence (AI) Approaches for Industrial Applications; Vajjhala, N.R., Roy, S.S., Taşcı, B., et al., Eds.; Springer Nature: Cham, Switzerland, 2025; pp. 189–221.
  6. Mohapatra, A.; Mohanty, P.; Pattnaik, M.; et al. Physiotherapy in the digital age: A narrative review of the paradigm shift driven by the integration of artificial intelligence and machine learning. Physiother. J. Indian Assoc. Physiother. 2024, 18, 63–71.
  7. Physitrack. Available online: https://www.physitrack.com (accessed on 6 August 2025).
  8. Arensman, R.; Kloek, C.; Pisters, M.; et al. Patient perspectives on using a smartphone app to support home-based exercise during physical therapy treatment: qualitative study. JMIR Hum. Factors 2022, 9, e35316.
  9. Doxy.me. Available online: https://doxy.me (accessed on 6 August 2025).
  10. Schuler, K.R.; Ong, T.; Welch, B.M.; et al. Examining a Telemedicine-Based Virtual Reality Clinic in Treating Adults with Specific Phobia: Protocol for a Feasibility Randomized Controlled Efficacy Trial. JMIR Res. Protoc. 2025, 14, e65770.
  11. PHZIO. Available online: https://phzio.com (accessed on 6 August 2025).
  12. Huber, M.; Rabin, B.; Docan, C.; et al. PlayStation 3-based tele-rehabilitation for children with hemiplegia. In Proceedings of the 2008 Virtual Rehabilitation, Vancouver, BC, Canada, 25–27 August 2008; pp. 105–112.
  13. Chen, H.L.; Lin, S.Y.; Yeh, C.F.; et al. Development and feasibility of a Kinect-based constraint-induced therapy program in the home setting for children with unilateral cerebral palsy. Front. Bioeng. Biotechnol. 2021, 9, 755506.
  14. Huang, J.D. Kinerehab: a kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. In Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility, Dundee, UK, 24–26 October 2011; pp. 319–320.
  15. Baqai, A.; Memon, K.; Memon, A.R.; et al. Interactive physiotherapy: An application based on virtual reality and bio-feedback. Wirel. Pers. Commun. 2019, 106, 1719–1741.
  16. Technalia. Available online: https://www.tecnalia.com (accessed on 6 August 2025).
  17. Garzo, A.; Jung, J.H.; Arcas-Ruiz-Ruano, J.; et al. Armassist: a telerehabilitation solution for upper-limb rehabilitation at home. IEEE Robot. Autom. Mag. 2022, 30, 62–71.
  18. Rehab-Robotics. Available online: https://www.rehab-robotics.com.hk (accessed on 6 August 2025).
  19. Hand of Hope Brochure. Available online: https://www.rehab-robotics.com.hk/hoh/RM-230-HOH3-0001-7%20HOH_brochure_eng.pdf (accessed on 6 August 2025).
  20. Suppiah, R.; Sharma, A.; Kim, N.; et al. An Electromyography-aided Robotics Hand for Rehabilitation–A Proof-of-Concept Study. In Proceedings of the 2020 IEEE Region 10 Conference (TENCON), Osaka, Japan, 16–19 November 2020; pp. 361–366.
  21. VisioRoller Brochure. Available online: https://www.rehab-robotics.com.hk/visioroller/VisioRoller.pdf (accessed on 6 August 2025).
  22. Bilic, D.; Uzunovic, T.; Golubovic, E.; et al. Internet of things-based system for physical rehabilitation monitoring. In Proceedings of the 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, Bosnia and Herzegovina, 26–28 October 2017; pp. 1–6.
  23. Fook, V.F.; Hao, S.Z.; Wai, A.A.; et al. Innovative platform for tele-physiotherapy. In Proceedings of HealthCom 2008-10th International Conference on e-health Networking, Singapore, 7–9 July 2008; pp. 59–65.
  24. Kaia Health. Available online: https://www.kaiahealth.com (accessed on 6 August 2025).
  25. Biebl, J.T.; Rykala, M.; Strobel, M.; et al. App-based feedback for rehabilitation exercise correction in patients with knee or hip osteoarthritis: prospective cohort study. J. Med. Internet Res. 2021, 23, e26658.
  26. DorsaVi. Available online: https://dorsavi.com (accessed on 6 August 2025).
  27. Chang, R.P.; Smith, A.; Kent, P.; et al. Concurrent validity of DorsaVi wireless motion sensor system Version 6 and the Vicon motion analysis system during lifting. BMC Musculoskelet. Disord. 2022, 23, 909.
  28. Khanghah, A.B.; Fernie, G.; Fekr, A.R. A novel approach to tele-rehabilitation: implementing a biofeedback system using machine learning algorithms. Mach. Learn. Appl. 2023, 14, 100499.
  29. Ramalingam, V.; Anand, T.; Pavithran, P. MEMS Sensor based Online Physiotherapy System. J. Adv. Sci. Technol. 2016, 5, 36–41.
  30. Roberts, G.; Gao, J. PyTracker: A low-cost mobile platform for telerehabilitation. In Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 12–14 December 2018; pp. 336–341.
  31. Ongvisatepaiboon, K.; Chan, J.H.; Vanijja, V. Smartphone-based tele-rehabilitation system for frozen shoulder using a machine learning approach. In Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa, 7–10 December 2015; pp. 811–815.
  32. Anwar, S.; Prasad, R. Framework for future telemedicine planning and infrastructure using 5G technology. Wirel. Pers. Commun. 2018, 100, 193–208.
  33. Bhattacharya, S. The impact of 5g technologies on healthcare. Indian J. Surg. 2023, 85, 531–535.
  34. Williamson, J.; Liu, Q.; Lu, F.; et al. Data sensing and analysis: Challenges for wearables. In Proceedings of the 20th Asia and South Pacific Design Automation Conference, Chiba, Japan, 19–22 January 2015; pp. 136–141.
  35. Malik, S.; Gupta, K.; Gupta, D.; et al. Intelligent load-balancing framework for fog-enabled communication in healthcare. Electronics 2022, 11, 566.
  36. Khullar, V.; Singh, H.P.; Miro, Y.; et al. IoT fog-enabled multi-node centralized ecosystem for real time screening and monitoring of health information. Appl. Sci. 2022, 12, 9845.
  37. Kakkar, L.; Gupta, D.; Tanwar, S.; et al. A secure and efficient signature scheme for IoT in healthcare. Comput. Mater. Contin. 2022, 73, 6151–6168.
  38. Qi, P.; Chiaro, D.; Giampaolo, F.; et al. A blockchain-based secure Internet of medical things framework for stress detection. Inf. Sci. 2023, 628, 377–390.